Examining Plant Uptake and Translocation of Emerging Contaminants using Machine Learning: Implications to Food Security
Abstract
When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log Kow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.
Recommended Citation
M. Bagheri et al., "Examining Plant Uptake and Translocation of Emerging Contaminants using Machine Learning: Implications to Food Security," Science of the Total Environment, vol. 698, Elsevier B.V., Jan 2020.
The definitive version is available at https://doi.org/10.1016/j.scitotenv.2019.133999
Department(s)
Electrical and Computer Engineering
Second Department
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for Research in Energy and Environment (CREE)
Third Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Clustering algorithm; Food security; Fuzzy logic; Neural network; Plant uptake
International Standard Serial Number (ISSN)
0048-9697; 1879-1026
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier B.V., All rights reserved.
Publication Date
01 Jan 2020
Comments
This work was supported by National Science Foundation under Award Number 1606036, the Mary K. Finley Endowment , and the Missouri S&T Intelligent Systems Center. The research was also sponsored by the Army Research Laboratory (ARL), and it was accomplished under Cooperative Agreement Number W911NF-18-2-0260.